ai-ml

Fish Audio

da-okazaki

by da-okazaki

Convert text to speech with Fish Audio. Use our AI voice generator for real-time, high-quality speech to voice, free for

Integrates with Fish Audio's API to generate high-quality speech from text with configurable voice models, audio formats, and real-time streaming for creating conversational applications and automated content narration.

github stars

10

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Multilingual supportReal-time streaming capabilityVoice cloning features

best for

  • / Building conversational AI applications
  • / Automated content narration and voiceovers
  • / Creating multilingual speech synthesis
  • / Real-time voice generation for chatbots

capabilities

  • / Generate speech from text using AI voice models
  • / Stream audio in real-time for low-latency applications
  • / Select voices by ID, name, or tags from voice library
  • / Export audio in multiple formats (MP3, WAV, PCM, Opus)
  • / Clone and create custom voice models
  • / Control speech prosody and emotions

what it does

Converts text to speech using Fish Audio's API with support for multiple voice models, streaming, and various audio formats.

about

Fish Audio is a community-built MCP server published by da-okazaki that provides AI assistants with tools and capabilities via the Model Context Protocol. Convert text to speech with Fish Audio. Use our AI voice generator for real-time, high-quality speech to voice, free for It is categorized under ai ml.

how to install

You can install Fish Audio in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Fish Audio is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Fish Audio MCP Server

<div align="center"> <img src="./dcos/icon_fish-audio.webp" alt="Fish Audio Logo" width="300" height="300" /> </div>

npm version License: MIT

An MCP (Model Context Protocol) server that provides seamless integration between Fish Audio's Text-to-Speech API and LLMs like Claude, enabling natural language-driven speech synthesis.

What is Fish Audio?

Fish Audio is a cutting-edge Text-to-Speech platform that offers:

  • 🌊 State-of-the-art voice synthesis with natural-sounding output
  • 🎯 Voice cloning capabilities to create custom voice models
  • 🌍 Multilingual support including English, Japanese, Chinese, and more
  • Low-latency streaming for real-time applications
  • 🎨 Fine-grained control over speech prosody and emotions

This MCP server brings Fish Audio's powerful capabilities directly to your LLM workflows.

Features

  • 🎙️ High-Quality TTS: Leverage Fish Audio's state-of-the-art TTS models
  • 🌊 Streaming Support: Real-time audio streaming for low-latency applications
  • 🎨 Multiple Voices: Support for custom voice models via reference IDs
  • 🎯 Smart Voice Selection: Select voices by ID, name, or tags
  • 📚 Voice Library Management: Configure and manage multiple voice references
  • 🔧 Flexible Configuration: Environment variable-based configuration
  • 📦 Multiple Audio Formats: Support for MP3, WAV, PCM, and Opus
  • 🚀 Easy Integration: Simple setup with any MCP-compatible client

Quick Start

Installation

You can run this MCP server directly using npx:

npx @alanse/fish-audio-mcp-server

Or install it globally:

npm install -g @alanse/fish-audio-mcp-server

Configuration

  1. Get your Fish Audio API key from Fish Audio

  2. Set up environment variables:

export FISH_API_KEY=your_fish_audio_api_key_here
  1. Add to your MCP settings configuration:

Single Voice Mode (Simple)

{
  "mcpServers": {
    "fish-audio": {
      "command": "npx",
      "args": ["-y", "@alanse/fish-audio-mcp-server"],
      "env": {
        "FISH_API_KEY": "your_fish_audio_api_key_here",
        "FISH_MODEL_ID": "speech-1.6",
        "FISH_REFERENCE_ID": "your_voice_reference_id_here",
        "FISH_OUTPUT_FORMAT": "mp3",
        "FISH_STREAMING": "false",
        "FISH_LATENCY": "balanced",
        "FISH_MP3_BITRATE": "128",
        "FISH_AUTO_PLAY": "false",
        "AUDIO_OUTPUT_DIR": "~/.fish-audio-mcp/audio_output"
      }
    }
  }
}

Multiple Voice Mode (Advanced)

{
  "mcpServers": {
    "fish-audio": {
      "command": "npx",
      "args": ["-y", "@alanse/fish-audio-mcp-server"],
      "env": {
        "FISH_API_KEY": "your_fish_audio_api_key_here",
        "FISH_MODEL_ID": "speech-1.6",
        "FISH_REFERENCES": "[{'reference_id':'id1','name':'Alice','tags':['female','english']},{'reference_id':'id2','name':'Bob','tags':['male','japanese']},{'reference_id':'id3','name':'Carol','tags':['female','japanese','anime']}]",
        "FISH_DEFAULT_REFERENCE": "id1",
        "FISH_OUTPUT_FORMAT": "mp3",
        "FISH_STREAMING": "false",
        "FISH_LATENCY": "balanced",
        "FISH_MP3_BITRATE": "128",
        "FISH_AUTO_PLAY": "false",
        "AUDIO_OUTPUT_DIR": "~/.fish-audio-mcp/audio_output"
      }
    }
  }
}

Environment Variables

VariableDescriptionDefaultRequired
FISH_API_KEYYour Fish Audio API key-Yes
FISH_MODEL_IDTTS model to use (s1, speech-1.5, speech-1.6)s1Optional
FISH_REFERENCE_IDDefault voice reference ID (single reference mode)-Optional
FISH_REFERENCESMultiple voice references (see below)-Optional
FISH_DEFAULT_REFERENCEDefault reference ID when using multiple references-Optional
FISH_OUTPUT_FORMATDefault audio format (mp3, wav, pcm, opus)mp3Optional
FISH_STREAMINGEnable streaming mode (HTTP/WebSocket)falseOptional
FISH_LATENCYLatency mode (normal, balanced)balancedOptional
FISH_MP3_BITRATEMP3 bitrate (64, 128, 192)128Optional
FISH_AUTO_PLAYAuto-play audio and enable real-time playbackfalseOptional
AUDIO_OUTPUT_DIRDirectory for audio file output~/.fish-audio-mcp/audio_outputOptional

Configuring Multiple Voice References

You can configure multiple voice references in two ways:

JSON Array Format (Recommended)

Use the FISH_REFERENCES environment variable with a JSON array:

FISH_REFERENCES='[
  {"reference_id":"id1","name":"Alice","tags":["female","english"]},
  {"reference_id":"id2","name":"Bob","tags":["male","japanese"]},
  {"reference_id":"id3","name":"Carol","tags":["female","japanese","anime"]}
]'
FISH_DEFAULT_REFERENCE="id1"

Individual Format (Backward Compatibility)

Use numbered environment variables:

FISH_REFERENCE_1_ID=id1
FISH_REFERENCE_1_NAME=Alice
FISH_REFERENCE_1_TAGS=female,english

FISH_REFERENCE_2_ID=id2
FISH_REFERENCE_2_NAME=Bob
FISH_REFERENCE_2_TAGS=male,japanese

Usage

Once configured, the Fish Audio MCP server provides two tools to LLMs.

Tool 1: fish_audio_tts

Generates speech from text using Fish Audio's TTS API.

Parameters

  • text (required): Text to convert to speech (max 10,000 characters)
  • reference_id (optional): Voice model reference ID
  • reference_name (optional): Select voice by name
  • reference_tag (optional): Select voice by tag
  • streaming (optional): Enable streaming mode
  • format (optional): Output format (mp3, wav, pcm, opus)
  • mp3_bitrate (optional): MP3 bitrate (64, 128, 192)
  • normalize (optional): Enable text normalization (default: true)
  • latency (optional): Latency mode (normal, balanced)
  • output_path (optional): Custom output file path
  • auto_play (optional): Automatically play the generated audio
  • websocket_streaming (optional): Use WebSocket streaming instead of HTTP
  • realtime_play (optional): Play audio in real-time during WebSocket streaming

Voice Selection Priority: reference_id > reference_name > reference_tag > default

Tool 2: fish_audio_list_references

Lists all configured voice references.

Parameters

No parameters required.

Returns

  • List of configured voice references with their IDs, names, and tags
  • Default reference ID

Examples

Basic Text-to-Speech

User: "Generate speech saying 'Hello, world! Welcome to Fish Audio TTS.'"

Claude: I'll generate speech for that text using Fish Audio TTS.

[Uses fish_audio_tts tool with text parameter]

Result: Audio file saved to ./audio_output/tts_2025-01-03T10-30-00.mp3

Using Custom Voice by ID

User: "Generate speech with voice model xyz123 saying 'This is a custom voice test'"

Claude: I'll generate speech using the specified voice model.

[Uses fish_audio_tts tool with text and reference_id parameters]

Result: Audio generated with custom voice model xyz123

Using Voice by Name

User: "Use Alice's voice to say 'Hello from Alice'"

Claude: I'll generate speech using Alice's voice.

[Uses fish_audio_tts tool with reference_name: "Alice"]

Result: Audio generated with Alice's voice

Using Voice by Tag

User: "Generate Japanese speech saying 'こんにちは' with an anime voice"

Claude: I'll generate Japanese speech with an anime-style voice.

[Uses fish_audio_tts tool with reference_tag: "anime"]

Result: Audio generated with anime voice style

List Available Voices

User: "What voices are available?"

Claude: I'll list all configured voice references.

[Uses fish_audio_list_references tool]

Result:
- Alice (id: id1) - Tags: female, english [Default]
- Bob (id: id2) - Tags: male, japanese
- Carol (id: id3) - Tags: female, japanese, anime

HTTP Streaming Mode

User: "Generate a long speech in streaming mode about the benefits of AI"

Claude: I'll generate the speech in streaming mode for faster response.

[Uses fish_audio_tts tool with streaming: true]

Result: Streaming audio saved to ./audio_output/tts_2025-01-03T10-35-00.mp3

WebSocket Real-time Streaming

User: "Stream and play in real-time: 'Welcome to the future of AI'"

Claude: I'll stream the speech via WebSocket and play it in real-time.

[Uses fish_audio_tts tool with websocket_streaming: true, realtime_play: true]

Result: Audio streamed and played in real-time via WebSocket

Development

Local Development

  1. Clone the repository:
git clone https://github.com/da-okazaki/mcp-fish-audio-server.git
cd mcp-fish-audio-server
  1. Install dependencies:
npm install
  1. Create .env file:
cp .env.example .env
# Edit .env with your API key
  1. Build the project:
npm run build
  1. Run in development mode:
npm run dev

Testing

Run the test suite:

npm test

Project Structure

mcp-fish-audio-server/
├── src/
│   ├── index.ts          # MCP server entry point
│   ├── tools/
│   │   └── tts.ts        # TTS tool implementation
│   ├── services/
│   │   └── fishAudio.ts  # Fish Audio API client
│   ├── types/
│   │   └── index.ts      # TypeScript definitions
│   └── utils/
│       └── config.ts     # Configuration management
├── tests/                # Test files
├── audio_output/         # Default audio output directory
├── package.json
├── tsconfig.json
└── README.md

API Documentation

Fish Audio Service

The service provides two main methods:

  1. generateSpeech: Standard TTS generation

    • Returns audio buffer
    • Suitable for short texts
    • Lower memory usage
  2. generateSpeechStream: Streamin


FAQ

What is the Fish Audio MCP server?
Fish Audio is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Fish Audio?
This profile displays 46 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.846 reviews
  • Neel Menon· Dec 28, 2024

    Fish Audio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Pratham Ware· Dec 24, 2024

    I recommend Fish Audio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Zara Huang· Dec 12, 2024

    Fish Audio reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Sakura Gill· Dec 4, 2024

    Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Hiroshi Verma· Nov 23, 2024

    I recommend Fish Audio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Kabir Liu· Nov 19, 2024

    According to our notes, Fish Audio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Sakshi Patil· Nov 15, 2024

    Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Neel Kim· Nov 15, 2024

    We evaluated Fish Audio against two servers with overlapping tools; this profile had the clearer scope statement.

  • Hiroshi Tandon· Nov 3, 2024

    Fish Audio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Hiroshi Patel· Oct 22, 2024

    Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.

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